There are many situations in which it may be necessary to extract a signal of interest from a noisy received signal. This task becomes more difficult in a situation in which the received signal has a low signal to noise ratio (SNR). In some cases, the signal of interest may be generated in response to a stimulus and may also be synchronized to the stimulus. An example of such a case relates to the measurement of evoked responses. Electrophysiological evoked responses to a variety of stimuli are known to contain valuable clinical and scientific information in the assessment of the sensorineural systems of humans and animals. Evoked responses (ER), such as, for example, auditory evoked potentials, somatosensory evoked potentials, visual evoked potentials, otoacoustic emissions, or the like, are signals that are often 10-1000 times smaller than the noise that is typically recorded by signal transducers (such as electrodes or microphones) at the time of recording the ER. In many cases, the ER waveform and its clinically relevant features may only be detectable after averaging thousands of responses to individual stimuli.
The noise that is recorded by the signal transducers may be caused by various sources, including, for example, noise generated by muscular activity, for example, EMG noise, or the like, during an evoked response (ER) test and may also include electrical noise from lighting, other instruments and the like. Because the noise is generally many times greater than the ER signal, the noise tends to mask the ER signal. One challenge of clinical ER measurement is determining whether specific features of an ER waveform represent true electrophysiological responses or if the specific features are a result of noise. A special application of ER detection is the detection of the auditory brainstem response (ABR) and auditory steady state responses (ASSR) with applications to infant hearing screening and to the determination of auditory thresholds for all ages, which may be used in the customized fitting of hearing aids.
Several conventional techniques used to minimize noise in the recorded response to auditory stimuli are known. These techniques include, for example, signal averaging and weighted signal averaging, signal filtering, artifact rejection, and various techniques designed to relax or sedate the subject.
Signal averaging involves stimulating the patient with multiple stimuli, obtaining multiple time-based data series, each data series synchronized to a single instance of the stimulus, and averaging the multiple synchronized data series. Limitations of this traditional averaging method in evoked potential acquisition have long been recognized. A problem may arise from a poor signal to noise ratio (SNR) and that the number of averages required typically increases in inverse proportion to the square of the SNR.
Artifact rejection (AR) can be used to eliminate a data series or groups of data series that are most contaminated with noise, by excluding from the average those data series for which the noise exceeds a preset threshold.
Weighted averaging (WA) may further improve SNR by weighting groups of data series in inverse proportion to their noise content. There are various conventional methods of assessing noise content of a group of data series to determine the weights. Assuming the noise is quasi-stationary, i.e. stationary within each group of data series, and independent between data series, weighting each group in inverse proportion to the variance of the noise within the group will minimize the squared error of the weighted average.
In a conventional example, a group of 250 responses to stimuli that were stimulated at a rate of 30 Hz can be examined and averaged. In this case, the group is greater than 8 seconds in duration. The drawback of this technique is that noise in evoked potential measurements is, in general, not stationary over an 8 second duration, especially when the time series is contaminated with interference from the patient's EMG caused by muscle activity. A further drawback where multiple groups of measurements are being made is that electrical noise in the environment is, in general, not independent from group to group, especially when a significant component of that noise is periodic or quasi-periodic such as noise arising from powerline interference or from coherent cortical EEG during deep sleep, or the like. For example, coherent or quasi-coherent EEG noise in the alpha band is particularly large under anesthesia, making the detection of cortical evoked potentials that contain significant frequency content in the alpha band particularly difficult.
An improvement to an averaging scheme or weighted averaging scheme may include using normative data for the ABR signal and EEG to estimate the magnitude of the noise component of the variance in the data series which is comprised of both signal and additive noise. If the signal model based on normative data is accurate, this technique allows estimation of the noise from individual data series instead of groups of data series. For this technique to be valid, the stationarity assumption may only be required for the duration of a single data series or response, typically, less than 100 ms. However, normative data is generally based on stimulus type and stimulus level and, in at least some cases, the noise might not necessarily be stationary, even at such a small duration.
In a different conventional approach, weights may be chosen to be inversely related to a measure of dissimilarity between individual data series and the estimated average. In an example, the weights may be inversely proportional to the mean squared error between each individual data series and the averaged signal estimate.
Overall, similar to other conventional methods noted above, the weighted techniques operate under the assumption that the noise from data series to data series is independent, i.e. the noise between pair of data series has zero covariance. If this independence assumption is not valid, the resulting weights will not be optimal in the sense that the mean squared noise in the weighted average will not be minimized. In evoked response signals, the independence assumption is generally not valid because of environmental noise, when present, such as sinusoidal noise arising from power-line frequencies and their harmonics, which are generally not independent and non-stationary.
Embodiments of the apparatus, system and method described herein are intended to address at least one of the difficulties of conventional methods of detecting an evoked response signal.